---
title: "Report"
author:
- familyname: Wang
  othernames: Yiru
  address: Monash Universtidy
  email: "ywan0553@student.monash.edu"
  correspondingauthor: true
  qualifications: section1
- familyname: Xu
  othernames: Kexin
  address: Monash University
  email: "kxuu0029@student.monash.edu"
  qualifications: section2
  correspondingauthor: true
- familyname: Tang
  othernames: Ruiqi
  address: Monash University
  email: "rtan00062student.monash.edu"
  correspondingauthor: true
  qualifications: section3
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
    source_code: embed
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(fig.align = "center")
```



```{r, echo = FALSE, message = FALSE, warning = FALSE}
# Libraries
library(flexdashboard)
library(tinytex)
library(gridExtra)
library(tidyverse)
library(readr)
library(bookdown)
library(knitr)
library(plotly)
library(kableExtra)
library(readxl)
```

State {data-icon="fa-globe"}
=============================
 

Row {data-height=1000}
-------------------------------------
    
### Chart 1

```{r NewSouthWales}
NewSouthWales <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 3,range = "A5:AB30")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Count") %>%
  mutate(State = "NewSouthWales")%>%
  select(Offence,Year,Count,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))
```

```{r NewSouthWales-rate,message=FALSE}
NewSouthWales_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 3,range = "A31:AB41")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Rate") %>%
  mutate(State = "NewSouthWales")%>%
  select(Offence,Year,Rate,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```



```{r Victoria}
Victoria <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 4,range = "A5:AB28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Count") %>%
  mutate(State = "Victoria")%>%
  select(Offence,Year,Count,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))
```

```{r Victoria_rate,message=FALSE}
Victoria_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 4,range = "A29:AB37")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Rate") %>%
 mutate(State = "Victoria")%>%
  select(Offence,Year,Rate,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```



```{r Queensland}
Queensland <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 5,range = "A5:AB28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Count") %>%
  mutate(State = "Queensland")%>%
  select(Offence,Year,Count,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))
```

```{r Queensland_rate,message=FALSE}
Queensland_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 5,range = "A29:AB37")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Rate") %>%
  mutate(State = "Queensland")%>%
  select(Offence,Year,Rate,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```



```{r South_Australia}
South_Australia <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 6,range = "A5:AB30")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Count") %>%
  mutate(State = "South Australia")%>%
  select(Offence,Year,Count,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))
```


```{r South_Australia_rate,message=FALSE}
South_Australia_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 6,range = "A31:AB41")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Rate") %>%
mutate(State = "South Australia")%>%
  select(Offence,Year,Rate,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```


```{r Western_Australia}
Western_Australia <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 7,range = "A5:AB30")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Count") %>%
  mutate(State = "Western Australia")%>%
  select(Offence,Year,Count,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))
```

```{r Western_Australia_rate,message=FALSE}
Western_Australia_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 7,range = "A31:AB41")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Rate") %>%
  mutate(State = "Western Australia")%>%
  select(Offence,Year,Rate,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```


```{r Tasmania1}
Tasmania1 <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", 
                       sheet = 8,range = "A5:AB22")%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))%>%
  select(!("1993":"2009"))%>%
  pivot_longer(cols = '2010':'2013',  
               names_to = "Year",            
               values_to = "Count") %>%
  mutate(Count = as.double(Count))%>%
 mutate(State = "Tasmania")%>%
  select(Offence,Year,Count,State)
```

```{r Tasmania}
Tasmania2 <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", 
                       sheet = 8,range = "A5:AB22")%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))%>%
  select(!("1993":"2013"))%>%
  pivot_longer(cols = '2014':'2019',  
               names_to = "Year",            
               values_to = "Count") %>%
 mutate(State = "Tasmania")%>%
 select(Offence,Year,Count,State)
Tasmania <- bind_rows(Tasmania1,Tasmania2)
```

```{r Tasmania_rate1,message=FALSE}
Tasmania_rate1 <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 8,range = "A31:AB41")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))%>%
  pivot_longer(cols = '2010':'2013',  
               names_to = "Year",            
               values_to = "Rate") %>%
   mutate(Rate = as.double(Rate))%>%
 mutate(State = "Tasmania")%>%
  select(Offence,Year,Rate,State)
```

```{r Tasmania_rate,message=FALSE}
Tasmania_rate2 <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 8,range = "A31:AB41")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))%>%
  pivot_longer(cols = '2014':'2019',  
               names_to = "Year",            
               values_to = "Rate") %>%
 mutate(State = "Tasmania")%>%
  select(Offence,Year,Rate,State)
Tasmania_rate <- bind_rows(Tasmania_rate1,Tasmania_rate2)
```


```{r Northern_Territory}
Northern_Territory <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 9,range = "A5:AB30")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Count") %>%
  mutate(State = "Northern Territory")%>%
  select(Offence,Year,Count,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))
```

```{r Northern_Territory_rate,message=FALSE}
Northern_Territory_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 10,range = "A31:AB41")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Rate") %>%
  mutate(State = "Northern Territory")%>%
  select(Offence,Year,Rate,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```


```{r Capital}
Capital <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 10,range = "A5:AB30")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Count") %>%
  mutate(State = "Capital")%>%
  select(Offence,Year,Count,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))
```

```{r Capital_rate,message=FALSE}
Capital_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 10,range = "A31:AB41")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Rate") %>%
  mutate(State = "Capital")%>%
  select(Offence,Year,Rate,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```


```{r State}
State <- bind_rows(NewSouthWales,Victoria,Queensland,South_Australia,Western_Australia,Tasmania,Northern_Territory,Capital)
```

```{r}
State_rate <- bind_rows(NewSouthWales_rate,Victoria_rate,Queensland_rate,South_Australia_rate,Western_Australia_rate,Tasmania_rate,Northern_Territory_rate,Capital_rate)
```


```{r plot1,fig.height = 9, fig.width=10,fig.cap="Comparison of the number of victims of various crimes in each state in the same year"}
State %>%
  ggplot(aes(x = Offence,
             y = Count,
             fill = State))+
  geom_bar(stat  = "identity", position = "dodge") + 
  ggtitle("Comparison of the number of victims of various crimes in each state in the same year") +
  facet_wrap(~Year, ncol = 1,scales= "free")
```

Row {data-height=1000}
-------------------------------------
    
### Chart 2

```{r plot2,fig.height = 8, fig.width=8, fig.cap="Rate of change in the number of victims of various crimes by state"}
State %>%
  ggplot(aes( x = Year,     
              y = Count, 
              color = Offence,
              group = Offence)) + 
  geom_line(stat = "identity")  +
  theme(axis.title.x =element_text(size=14), axis.title.y=element_text(size=14)) +
   ggtitle("Rate of change in the number of victims of various crimes by state") +
  facet_wrap(~State, ncol = 1,scales= "free")
```


### Chart 3

```{r}
State_count1<-State %>%
  filter(Offence == "Sexual assault")%>%
  pivot_wider(id_cols = State,  
              names_from = Year,  
              values_from = Count)%>%
  mutate("Sexual assault_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
  mutate("Sexual assault_change" = (`2019` - `2010`))%>%
  select(!`2010`:`2019`)
State_count2<-State %>%
  filter(Offence == "Murder")%>%
  pivot_wider(id_cols = State,  
              names_from = Year,  
              values_from = Count)%>%
  mutate("Murder_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
   mutate("Murder_change" = (`2019` - `2010`))%>%
  select(!`2010`:`2019`)
State_count3<-State %>%
  filter(Offence == "Armed robbery")%>%
  pivot_wider(id_cols = State,  
              names_from = Year,  
              values_from = Count)%>%
  mutate("robbery_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
   mutate("robbery_change" = (`2019` - `2010`))%>%
  select(!`2010`:`2019`)
State_count4<-State %>%
  filter(Offence == "Manslaughter")%>%
  pivot_wider(id_cols = State,  
              names_from = Year,  
              values_from = Count)%>%
  mutate("Manslaughter_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
  mutate("Manslaughter_change" = (`2019` - `2010`))%>%
  select(!`2010`:`2019`)
State_count5<-State %>%
  filter(Offence == "Kidnapping/abduction")%>%
  pivot_wider(id_cols = State,  
              names_from = Year,  
              values_from = Count)%>%
  mutate("Kidnapping_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
   mutate("Kidnapping_change" = (`2019` - `2010`))%>%
  select(!`2010`:`2019`)
State_count6<-State %>%
  filter(Offence == "Blackmail/extortion")%>%
  pivot_wider(id_cols = State,  
              names_from = Year,  
              values_from = Count)%>%
  mutate("extortion_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
    mutate("extortion_change" = (`2019` - `2010`))%>%
  select(!`2010`:`2019`)

```


```{r table1,fig.height = 4 , fig.width=6}
state_sum1<-merge(State_count1,State_count2, by= 'State')
state_sum<-merge(State_count3,state_sum1, by= 'State')
knitr::kable(state_sum,caption = 'Ten-year rate of change in the number of victims of various crimes by state',booktabs = TRUE)%>%
   kable_styling(bootstrap_options = c("striped", "hover"))
```

```{r table2}
state_sum2<-merge(State_count4,State_count5, by= 'State')
state_sum3<-merge(State_count6,state_sum2,by= 'State')
knitr::kable(state_sum3,caption = 'Ten-year rate of change in the number of victims of various crimes by state',booktabs = TRUE)%>%
   kable_styling(bootstrap_options = c("striped", "hover"))
```

Row {data-height=600}
-------------------------------------

### Chart 4
    
```{r plot3,fig.height = 5 ,fig.width=7,fig.cap="The rate of change in the rate of various crime victims by state"}
State_rate %>%
  filter(Offence == "Sexual assault")%>%
  ggplot(aes( x = Year,     
              y = Rate, 
              color = State,
              group = State)) + 
  geom_line(stat = "identity")  +
  ylab("rate-Victims per 100,000")+
  theme(axis.title.x =element_text(size=14), axis.title.y=element_text(size=14)) +
   ggtitle("Rate of change in the number of victims of various crimes by state") 
```



### Chart 5
   
```{r table3}
State_rate1<-State_rate %>%
  filter(Offence == "Sexual assault")%>%
  pivot_wider(id_cols = State,  
              names_from = Year,  
              values_from = Rate)%>%
  mutate("2010-2011" = (`2019` - `2010`))%>%
  mutate("rate_change" =  ((`2019` - `2010`)/`2010`)*100)%>%
  select(!`2010`:`2019`)
 knitr::kable(
State_rate1, booktabs = TRUE,
  caption = 'A 10-year change in the proportion of victims of sexual assault')%>%
   kable_styling(bootstrap_options = c("striped", "hover"))
```
    


Gender {data-icon="fa-user-plus"}
==================
Column{data-width=400}
--------

### Total number by gender {data-width=350}
  
```{r echo=FALSE}
Victims_of_Crime_raw <- readxl::read_excel("data/Victims_of_Crime_Australia.xls", sheet = 3, skip = 4, col_names = FALSE)
Victims_of_Crime <- Victims_of_Crime_raw[-1,1:11]
colnames(Victims_of_Crime) = Victims_of_Crime[1,]
Victims_of_Crime <- Victims_of_Crime[-1,]
Homicide <- Victims_of_Crime[-1,] %>% 
  slice(c(1:18)) %>% 
  filter(`Sex and age` == "Total")
Homicide[1,1] <- "Homicide_Male"
Homicide[2,1] <- "Homicide_Female"
Homicide[3,1] <- "Homicide_All"
Homicide <- Homicide %>% rename("Type_Gender" = `Sex and age`)
Murder <- Victims_of_Crime %>% 
  slice(c(21:38)) %>% 
  filter(`Sex and age` == "Total")
Murder[1,1] <- "Murder_Male"
Murder[2,1] <- "Murder_Female"
Murder[3,1] <- "Murder_All"
Murder <- Murder %>% rename("Type_Gender" = `Sex and age`)
Attemptedmurder <- Victims_of_Crime %>% 
  slice(c(40:57)) %>% 
  filter(`Sex and age` == "Total")
Attemptedmurder[1,1] <- "Attemptedmurder_Male"
Attemptedmurder[2,1] <- "Attemptedmurder_Female"
Attemptedmurder[3,1] <- "Attemptedmurder_All"
Attemptedmurder <- Attemptedmurder %>% rename("Type_Gender" = `Sex and age`)
Manslaughter <- Victims_of_Crime %>% 
  slice(c(59:76)) %>% 
  filter(`Sex and age` == "Total")
Manslaughter[1,1] <- "Manslaughter_Male"
Manslaughter[2,1] <- "Manslaughter_Female"
Manslaughter[3,1] <- "Manslaughter_All"
Manslaughter <- Manslaughter %>% rename("Type_Gender" = `Sex and age`)
Sexualassault <- Victims_of_Crime %>% 
  slice(c(78:110)) %>% 
  filter(`Sex and age` == "Total")
Sexualassault[1,1] <- "Sexualassault_Male"
Sexualassault[2,1] <- "Sexualassault_Female"
Sexualassault[3,1] <- "Sexualassault_All"
Sexualassault <- Sexualassault %>% rename("Type_Gender" = `Sex and age`)
Kidnappingabduction <- Victims_of_Crime %>% 
  slice(c(112:141)) %>% 
  filter(`Sex and age` == "Total")
Kidnappingabduction[1,1] <- "Kidnappingabduction_Male"
Kidnappingabduction[2,1] <- "Kidnappingabduction_Female"
Kidnappingabduction[3,1] <- "Kidnappingabduction_All"
Kidnappingabduction <- Kidnappingabduction %>% rename("Type_Gender" = `Sex and age`)
Robbery <- Victims_of_Crime %>% 
  slice(c(143:175)) %>% 
  filter(`Sex and age` == "Total")
Robbery[1,1] <- "Robbery_Male"
Robbery[2,1] <- "Robbery_Female"
Robbery[3,1] <- "Robbery_All"
Robbery <- Robbery %>% rename("Type_Gender" = `Sex and age`)
Armedrobbery <- Victims_of_Crime %>% 
  slice(c(177:209)) %>% 
  filter(`Sex and age` == "Total")
Armedrobbery[1,1] <- "Armedrobbery_Male"
Armedrobbery[2,1] <- "Armedrobbery_Female"
Armedrobbery[3,1] <- "Armedrobbery_All"
Armedrobbery <- Armedrobbery %>% rename("Type_Gender" = `Sex and age`)
Unarmedrobbery <- Victims_of_Crime %>% 
  slice(c(211:243)) %>% 
  filter(`Sex and age` == "Total")
Unarmedrobbery[1,1] <- "Unarmedrobbery_Male"
Unarmedrobbery[2,1] <- "Unarmedrobbery_Female"
Unarmedrobbery[3,1] <- "Unarmedrobbery_All"
Unarmedrobbery <- Unarmedrobbery %>% rename("Type_Gender" = `Sex and age`)
Blackmailextortion <- Victims_of_Crime %>% 
  slice(c(245:274)) %>% 
  filter(`Sex and age` == "Total")
Blackmailextortion[1,1] <- "Blackmailextortion_Male"
Blackmailextortion[2,1] <- "Blackmailextortion_Female"
Blackmailextortion[3,1] <- "Blackmailextortion_All"
Blackmailextortion <- Blackmailextortion %>% rename("Type_Gender" = `Sex and age`)
Victims_of_Crime_tidy <- bind_rows(Homicide, Murder, Attemptedmurder, Manslaughter, Sexualassault, Kidnappingabduction, Robbery, Armedrobbery, Unarmedrobbery, Blackmailextortion) %>% 
  mutate(`2010` = as.numeric(`2010`)) %>% 
  pivot_longer(cols = -Type_Gender, 
               names_to = "Year",
               values_to = "Number") %>% 
  separate(col = Type_Gender,
           into = c("Type", "Gender"), "_") %>% 
  filter(!Type == "Homicide",
         !Type == "Robbery")
```

```{r echo=FALSE}
g <- Victims_of_Crime_tidy %>% 
  group_by(Year, Gender) %>%
  summarise(sum = sum(Number)) %>% 
  ggplot(aes(x = Year,
             y = sum,
             color = Gender,
             group = Gender)) +
  geom_line() +
  theme_bw() +
  scale_color_brewer(palette = "Dark2") + 
  theme(legend.position = "bottom")
ggplotly(g)
```


### Analysis{data-width=250}  
  
* On the whole, it shows an upward trend year by year recently.  
* The number of female victims is much higher than that of male victims, almost twice.  

* With the exception of sexual assault and kidnapping, the majority of victims are men.
* The vast majority of cases are sexual assaults.  
* The number of robberies among the remaining crime categories is also considerable. (Including both armed and unarmed)  
* Possible reasons:
  + Women generally have stronger safety awareness
  + Women are less likely to be seen alone at night in areas where they are likely to be robbed  
    
According to statistics of gender-specific crimes in various countries, male victims are the majority in almost all crimes except for sex-related crimes in which more women are victims.
  



Column{data-width=400}
---------------


### Number by gender by crime type {data-width=400}

```{r fig.height = 8, echo=FALSE}
Victims_of_Crime_tidy %>% 
  ggplot() +
  geom_col(aes(x = Year,
                y = Number,
                fill = Gender),
           position = "dodge") +
  facet_grid(Type~., scales = "free_y") +
  scale_fill_brewer(palette = "Dark2") +
  theme_bw()
```




### Sexual assault proportion {data-width=450}



```{r echo=FALSE}
Victims_of_Crime_sum <- Victims_of_Crime_tidy %>% 
  filter(!Gender == "All") %>% 
  group_by(Gender, Year) %>% 
  summarise(sum = sum(Number))

Victims_of_Crime_tidy %>% 
  filter(Type == "Sexualassault" &
         !Gender == "All") %>% 
  left_join(Victims_of_Crime_sum) %>% 
  group_by(Gender, Year) %>% 
  summarise(sa_rate = Number/sum*100) %>% 
  kable(caption = "Sexual Assault Rate") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
```

Take sexual assault, female victims of sexual assault make up a very high proportion of total female victims.  

Column{data-width=400}
-----------

### Rate by gender{data-width=350}


```{r echo=FALSE}
Victims_of_Crime1 <- Victims_of_Crime_raw[-1,c(1, 12:21)]
colnames(Victims_of_Crime1) = Victims_of_Crime1[1,]
Victims_of_Crime1 <- Victims_of_Crime1[-1,]

Sexualassault1 <- Victims_of_Crime1 %>% 
  slice(c(78:110)) %>% 
  filter(`Sex and age` == "Total")
Sexualassault1[1,1] <- "Sexualassault_Male"
Sexualassault1[2,1] <- "Sexualassault_Female"
Sexualassault1[3,1] <- "Sexualassault_All"
Sexualassault1 <- Sexualassault1 %>% rename("Type_Gender" = `Sex and age`)

Sexualassault_rate <- Sexualassault1 %>% 
  mutate(`2010` = as.numeric(`2010`)) %>% 
  pivot_longer(cols = -Type_Gender, 
               names_to = "Year",
               values_to = "Rate") %>% 
  separate(col = Type_Gender,
           into = c("Type", "Gender"), "_") %>% 
  filter(!Gender == "All")
```


  

```{r echo=FALSE, fig.height=3, fig.width=5}
sa <- Sexualassault_rate %>%
  ggplot() +
  geom_col(aes(x = Year,
               y = Rate,
               fill = Gender),
               position = "dodge") +
  theme_bw() +
  scale_fill_brewer(palette = "Dark2") + 
  theme(legend.position = "bottom")

ggplotly(sa)
```
  
 
### Analysis 
  
* Rate is calculated by victim number divided by 100,000 persons.
* Show an upward pattern over years.
* Higher than 0.15% in recent years which means 3 out of every 2,000 people have been sexually assaulted.
* Possible reasons:
  + Women are physically vulnerable
  + Men are more sexually impulsive
  + Many women may choose not to report the crime
    - relationship to the offender
    - confidence in the justice system
    - fear of revenge
    - public opinion  
  
ABS personal safety survey also measures the number of women who contacted the police about the most recent incident within the last 10 years. Only 13.4% of women did so. 
   























Age {data-icon="fa-user-times"}
====================